Full Citation

Title: Interval Estimation of Potentially Missspecified Quantile Models

Citation Type: Working Paper

Publication Year: 2010

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Abstract: This paper develops practical methods for relaxing the missing at random assumption when estimatingmodels of conditional quantiles with missing outcome data and discrete covariates. We restrict theGHJUHHof non-ignorable selection governing the missingness process by imposing bounds on the Kolmogorov-Smirnov (KS) distance between the distribution of outcomes among missing observations and theoverall (unselected) distribution. Two methods are developed for conducting inference in this environment.The first allows us to perform finite sample inference on the identified set and is well suited to testsof model specification. The second enables us to conduct inference on the parameters of potentiallymisspecified models. To illustrate our techniques, we revisit the results of Angrist, Chernozhukov,and Fernandez-Val (2006) regarding changes across Decennial Censuses in the quantile specific returnsto schooling.

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Authors: Santos, Andres; Kline, Patrick

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Publication Number: w15716

Institution: National Bureau of Economic Research

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Publisher Location: Cambridge, MA

Data Collections: IPUMS CPS

Topics: Methodology and Data Collection

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